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Background Enhancing flag leaf nitrogen use efficiency (NUE) in wheat production can substantially increase crop productivity while minimizing nitrogen application. Quantitative trait loci (QTLs) for NUE-related have been rarely reported in wheat flag leaf traits. Results In this study, a natural population of 243 varieties and an RIL population of 123 F 7 recombinants were subjected to different nitrogen treatments. A genome-wide association study (GWAS) and linkage analysis were performed for four agronomic traits in terms of flag leaf length, flag leaf width, flag leaf area, and SPAD (chlorophyll content) under low and normal nitrogen conditions. Through GWAS, 1,016 significant SNP loci were identified and clustered into 290 QTLs, including 11 stably mapped QTLs (stable detection in multiple environments). Additionally, an AC population was established to verify the GWAS results and identify reliable QTL intervals. Three stable loci, namely, QFLLR6D.3 QFLWR6A.6 , and QSPADR5B.3 , were validated in the AC population, located 1.34 Mb, 2.84 Mb, and 5 Mb away from linkage mapping significant QTL, respectively. Through further transcriptome analysis of Chilero leaves at the jointing, anthesis and grain filling stages, four DEGs were identified within QSPADR5B.3 . Among them, TraesCS5B02G394300 , TraesCS5B02G394200 , and TraesCS5B02G39390 encode beta-glucosidases, and TraesCS5B02G396400 encodes a potassium channel. Conclusions These findings offer potential candidate genes for wheat breeding, and provide a foundation for exploring the molecular targets underlying wheat NUE.

With the swift progression of the High-Standard Farmland Construction Program in China and worldwide, many dryland wheat fields can be irrigated once during the wheat growth stage (one-off irrigation). However, the combined strategies of one-off irrigation, tillage, and N management for augmenting wheat grain yield and quality are still undeveloped in drought regions. Two-site split–split field experiments were conducted to study the impacts of irrigation, tillage, and N management and their combined effects on grain yield; the contents of protein and protein components; processing quality; and the characteristics of N accumulation and translocation in wheat from a typical dryland wheat production area in China from 2020 to 2022. The irrigation practices (I0, zero irrigation and I1, one-off irrigation), tillage methods (RT, rotary tillage; PT, plowing; and SS, subsoiling) and N management (N0, N120, N180, and N240) were applied to the main plots, subplots and sub-subplots, respectively. The experimental sites, experimental years, irrigation practices, tillage methods, and N management methods and their interaction significantly affected the yield, quality, and plant N characteristics of wheat in most cases. Compared to zero irrigation, one-off irrigation significantly increased the plant N accumulation, enhancing grain yield by 33.7% while decreasing the contents of total protein, albumin, globulin, gliadin, and glutenin by 4.4%, 6.4%, 8.0%, 12.2%, and 10.0%, respectively. It also decreased the wet gluten content, stability time, sedimentation value, extensibility by 4.1%, 10.7%, 9.7%, and 5.5%, respectively, averaged across sites and years. Subsoiling simultaneously enhanced the aforementioned indicators compared to rotary tillage and plowing in most sites and years. With the increase in N rates, wheat yield firstly increased and then decreased under zero irrigation combined with rotary tillage, while it gradually increased when one-off irrigation was combined with subsoiling; however, the contents of total protein and protein components and the quality tended to increase firstly and then stabilize regardless of irrigation practices and tillage methods. The correlations of yield and quality indicators with plant N characteristics were negative when using distinct irrigation practices and tillage methods, while they were positive under varying N management. The decrease in wheat quality induced by one-off irrigation could be alleviated by optimizing N management. I1STN180 exhibited higher yield, plant N accumulation and translocation, and better quality in most cases; thus, all metrics of wheat quality were significantly increased, with a yield enhancement of 50.3% compared to I0RTN180. Therefore, one-off irrigation with subsoiling and an N rate of 180 kg ha−1 is an optimal strategy for high yield, high protein, and high quality in dryland wheat production systems where one-off irrigation is assured.

The nitrogen (N) use efficiency (NUE) in the roots of seedlings is beneficial for increasing crop yield. Creating marker-assisted selection for wheat root traits can assist wheat breeders in choosing robust roots to maximize nutrient uptake. Exploring and identifying the effect of different N supply conditions on root system architecture (RSA) is of great significance for breeding N efficient wheat varieties. In this study, a total of 243 wheat varieties native to the Yellow and Huai Valley regions of China were utilized for genome-wide association studies (GWAS). Furthermore, a recombinant inbred line (RIL) population of 123 lines derived from the cross between Avocet and Chilero was utilized for linkage examination. A hydroponic seedling experiment using a 96-well tray was conducted in the lab with two treatments: normal N (NN) and low N (LN). Five RSA traits, including the relative number of root tips (RNRT), relative total root length (RTRL), relative total root surface area (RTRS), relative total root volume (RTRV), and relative average root diameter (RARD), were investigated. GWAS and linkage analysis were performed by integrating data from the wheat 660 k single nucleotide polymorphism (SNP) chip and diversity arrays technology (DArT) to identify genetic loci associated with RSA. The results showed that, based on the ratio of RSA-related traits under two N supply conditions, a total of 497 SNP markers, which are significantly associated with RSA-related traits, were detected at 148 genetic loci by GWAS. A total of 10 QTL loci related to RSA were discovered and identified by linkage mapping. Combining two gene localization methods, three colocalized intervals were found: AX-95160997/QRtrl.haust-3D, AX-109592379/QRnrt.haust-5A, and AX-110924288/QRtrl.haust-7D/QRtrs.haust-7D. According to the physical location of the colocalization of these two sites, between 39.61 and 43.74 Mb, 649.97 and 661.55 Mb, and 592.44 and 605.36 Mb are called qRtrl-3D, qRnrt-5A, and qRtrl-7D. This study has the potential to enhance the effectiveness of selecting root traits in wheat breeding programs, offering valuable insights into the genetic underpinnings of NUE in wheat. These results could help in breeding wheat varieties with higher NUE by implementing focused breeding strategies.

The winter wheat and summer maize double cropping system is the primary cropping pattern for wheat and maize in dryland areas of China. The management of tillage in this system is typically conducted before wheat sowing. However, few studies have validated and quantified the impact of tillage methods before wheat sowing and irrigation practices during the wheat season on the yield formation and water use efficiency of summer maize. Therefore, this study hypothesized that subsoiling before wheat sowing improves maize yield and WUE by enhancing soil moisture retention and plant development. A three-year field experiment with a two-factor split-plot design was conducted at the junction of the Loess Plateau and the Huang-Huai-Hai Plain in China for validation, from 2019 to 2022. Three tillage methods before wheat sowing (RT: rotary tillage; PT: plowing, SS: subsoiling) were assigned to the main plots, and two irrigation practices during wheat growing season (W0: zero-irrigation; W1: one-off irrigation) were assigned to subplots. We measured the soil moisture, grain yield, dry matter accumulation, nitrogen (N), phosphorus (P), and potassium (K) accumulation, and water use efficiency of summer maize. The results indicated that subsoiling before wheat sowing increased soil water storage at the sowing of summer maize, thereby promoting dry matter and nutrient accumulation. Compared to rotary tillage and plowing, subsoiling before wheat sowing increased grain yield and water use efficiency of maize by an average of 19.5% and 21.8%, respectively. One-off irrigation during the wheat season had negative effects on pre-sowing soil water storage and maize productivity in terms of yield and dry matter accumulation. However, subsoiling before wheat sowing can mitigate these negative effects of one-off irrigation. Correlation analysis and path model results indicated that tillage methods before wheat sowing had a greater impact on soil water storage and maize productivity than irrigation practices during wheat growing season. The most direct factor affecting maize yield was dry matter accumulation, whereas the most direct factor affecting water use efficiency was nutrient accumulation. The technique for order preference by similarity to an ideal solution (TOPSIS) comprehensive evaluation indicated that subsoiling before wheat sowing was superior for achieving high maize yield and water use efficiency under the practice of one-off irrigation during the wheat season. These findings offer practical guidance for optimizing soil water use and maize productivity in drylands.

The nitrogen (N) use efficiency (NUE) in the roots of seedlings is beneficial for increasing crop yield. Creating marker-assisted selection for wheat root traits can assist wheat breeders in choosing robust roots to maximize nutrient uptake. Exploring and identifying the effect of different N supply conditions on root system architecture (RSA) is of great significance for breeding N efficient wheat varieties. In this study, a total of 243 wheat varieties native to the Yellow and Huai Valley regions of China were utilized for genome-wide association studies (GWAS). Furthermore, a recombinant inbred line (RIL) population of 123 lines derived from the cross between Avocet and Chilero was utilized for linkage examination. A hydroponic seedling experiment using a 96-well tray was conducted in the lab with two treatments: normal N (NN) and low N (LN). Five RSA traits, including the relative number of root tips (RNRT), relative total root length (RTRL), relative total root surface area (RTRS), relative total root volume (RTRV), and relative average root diameter (RARD), were investigated. GWAS and linkage analysis were performed by integrating data from the wheat 660 k single nucleotide polymorphism (SNP) chip and diversity arrays technology (DArT) to identify genetic loci associated with RSA. The results showed that, based on the ratio of RSA-related traits under two N supply conditions, a total of 497 SNP markers, which are significantly associated with RSA-related traits, were detected at 148 genetic loci by GWAS. A total of 10 QTL loci related to RSA were discovered and identified by linkage mapping. Combining two gene localization methods, three colocalized intervals were found: AX-95160997/QRtrl.haust-3D, AX-109592379/QRnrt.haust-5A, and AX-110924288/QRtrl.haust-7D/QRtrs.haust-7D. According to the physical location of the colocalization of these two sites, between 39.61 and 43.74 Mb, 649.97 and 661.55 Mb, and 592.44 and 605.36 Mb are called qRtrl-3D, qRnrt-5A, and qRtrl-7D. This study has the potential to enhance the effectiveness of selecting root traits in wheat breeding programs, offering valuable insights into the genetic underpinnings of NUE in wheat. These results could help in breeding wheat varieties with higher NUE by implementing focused breeding strategies.

The debut of ChatGPT has recently attracted the attention of the natural language processing (NLP) community and beyond. Existing studies have demonstrated that ChatGPT shows significant improvement in a range of downstream NLP tasks, but the capabilities and limitations of ChatGPT in terms of recommendations remain unclear. In this study, we aim to conduct an empirical analysis of ChatGPT's recommendation ability from an Information Retrieval (IR) perspective, including point-wise, pair-wise, and list-wise ranking. To achieve this goal, we re-formulate the above three recommendation policies into a domain-specific prompt format. Through extensive experiments on four datasets from different domains, we demonstrate that ChatGPT outperforms other large language models across all three ranking policies. Based on the analysis of unit cost improvements, we identify that ChatGPT with list-wise ranking achieves the best trade-off between cost and performance compared to point-wise and pair-wise ranking. Moreover, ChatGPT shows the potential for mitigating the cold start problem and explainable recommendation. To facilitate further explorations in this area, the full code and detailed original results are open-sourced at https://github.com/rainym00d/LLM4RS.

We focus on the setting of contextual batched bandit (CBB), where a batch of rewards is observed from the environment in each episode. But the rewards of the non-executed actions are unobserved (i.e., partial-information feedbacks). Existing approaches for CBB usually ignore the rewards of the non-executed actions, resulting in feedback information being underutilized. In this paper, we propose an efficient reward imputation approach using sketching for CBB, which completes the unobserved rewards with the imputed rewards approximating the full-information feedbacks. Specifically, we formulate the reward imputation as a problem of imputation regularized ridge regression, which captures the feedback mechanisms of both the non-executed and executed actions. To reduce the time complexity of reward imputation, we solve the regression problem using randomized sketching. We prove that our reward imputation approach obtains a relative-error bound for sketching approximation, achieves an instantaneous regret with a controllable bias and a smaller variance than that without reward imputation, and enjoys a sublinear regret bound against the optimal policy. Moreover, we present two extensions of our approach, including the rate-scheduled version and the version for nonlinear rewards, making our approach more feasible. Experimental results demonstrated that our approach can outperform the state-of-the-art baselines on synthetic and real-world datasets.